SAP Community - BW SAP HANA Data Warehousing

End-To-End performance optimization in SAP Analytics Cloud with SAP Enterprise Support Services

2024-05-24T11:48:31.997000+02:00

Recently I posted a blog about SAP Analytics Cloud: Continuous Quality Checks (CQC services).

The overall performance of SAP Analytics Cloud (SAC) is mainly determined by the three components:

Client time - frontend
Network time
SAP Analytics Cloud service time - backend

It is important to regularly monitor and analyze the performance of SAP Analytics Cloud to identify and address any bottlenecks or inefficiencies.
The CQC services in my blog above offer great value in assisting you to analyze your system and/or situation based on real life data from your SAP Analytics Cloud Stories or Analytic Applications etc.
They do primarily focus on the frontend and network aspects.

In many cases the frontend can be the smaller portion of the total build time of a Story in SAP Analytics Cloud. When connecting to live SAP data sources you must ensure that these have been optimized for reporting usage. This can be achieved by optimizing database queries, tuning configurations and parameters, and scaling resources to handle increasing user loads. Additionally, implementing caching mechanisms, utilizing efficient data storage and retrieval techniques etc.

Getting to the Root Cause

To begin with you should perform some root cause analysis to narrow down where time is being spent.
Take a look at these useful resources and tooling within SAC

NEW Learning Journey: Managing Performance in SAP Analytics Cloud
2511489 - Troubleshooting performance issues in SAP Analytics Cloud
SAP Analytics Cloud Performance Decision Tree
SAP Analytics Cloud – Administration Cockpit
SAP Analytics Cloud Performance Statistics and Analysis
SAP Analytics Cloud Data Action Performance Statistics and Analysis
SAP Analytics Cloud - Performance Measurement

Once you establish the total build time and how much time is spent in the three components, you can follow the guidance in the links above to work on improving and optimizing each part.

SAP Enterprise Support Services - Backend

If you encounter a situation where you have performance impact coming from an SAP source system, such as SAP BW or SAP HANA, one option available to you is to engage with us and consume your entitlements from SAP Enterprise Support. We deliver specific recommendations via our CQC checks to help you fine tune these data sources.
Especially relevant for SAP BW and SAP HANA DB are:

Technical Performance Optimization (TPO)

The TPO service is designed to be an overall health check focusing on throughput of your database.
Some of the checks included in the service include:

Checks for major misconfigurations and hardware bottlenecks
Parameter settings, table space, expensive SQL statements
Database management (CBO statistics, Index and table storage management, Online log configuration)

For more details read the infosheet.

Business Process Performance Optimization (BPPO)

The CQC for SAP Business Process Performance Optimization is designed to ensure best performance for business-critical steps in your solution. It is more relevant for specific issues like modeling.
Some of the checks included in the service include:

Detailed technical analysis of critical process steps, evaluates and identifies potential improvement areas.
Investigation of long running process chains / failed process chains.
Investigation of long running BW queries
Parameter checks of the system for both hardware/software and suggestions for performance improvement

For more details read the infosheet

Further Support and Empowerment via SAP Enterprise Support Value Maps

SAP Enterprise Support Value Maps — your go-to team for foundational support and empowerment — have officially moved to a dedicated SAP Community Group.

As part of your SAP cloud subscription, you get access to:

Expert-led resources
Tailored recommendations
Advisory services
…all designed to help you accelerate innovation and success.

👉 Visit our Community Group
Sign in with your S-user ID or Universal ID, then hit “Subscribe” to stay updated.

Looking to dive deeper into data-driven innovation?
Within the group, explore the Data & Analytics Value Map to connect with your Foundational Support team. Simply click “Join Group”, and once inside, you can even request a call with a topic expert to get personalized guidance and enablement support.

Quicklinks

Q - the easy Installer for SAP IQ - ng 2024

2024-06-27T14:52:17.935000+02:00

last changed: 28th of June 2024

Q - the easy Installer for SAP IQ - ng 2024

Blog Content

as the rudimental HTML anker functionality is not available anymore in the current SAP Blog capabilities, I had to deactivate the TOC.

enable HTTPS Port 1129 for the SAP Host Agent

the Answer: Q – the easy Installer for SAP IQ

create DSN (.odbc.ini) with iqdsn

prepare the SSL/TLS IQ Setup

the new DBACOCKPIT for IQ

the SAP BW(/4) nearline storage configurationthe SAP S/4 ILM Store configuration

Beginning with 2017, the request for the Installation of SAP (Sybase) IQ grow for several reasons, everyone can understand also followed by the message - SAP Commitment to On-Premise SAP IQ Customers (May 2020)

from nearline storage to cold store - SAP & PBS - SAP HANA Data Lake -

private vs. public DataLake - SAP (Sybase) IQ – the hidden treasure …

reducing the memory food print on SAP HANA - btellegent - DataVard Glue - clariba -

better TCO for in-memory applications - GrayMatter

simplified data management - IT Central Station

System Properties Comparison SAP HANA vs. SAP IQ - DB Engines

who owns your own data - why SAP BW/4HANA?

SAP ILM with IQ - reducing memory footprint in S/4

Besides that, other bullet points came into the consideration:

structured access to unstructured content in Data Lake - SAP (Sybase) IQ – the hidden treasure …

streaming capabilities - Ignite vs. SAP IQ - ATTUNITY - AecorSoft -

performance alternative to existing Data Lake implementation - clariba - webobjects - askdata

Since 2013, the SAP First Guidance - SAP NLS Solution with SAP IQ 16.x became the "Almanack" and Blue Print for SAP IQ Database Implementations, despite for SAP or native usage.

Overview - SAP NLS Solution for BW(4) - Overview 2021

These SAP KBA Notes where created with the user roland.kramer@sap.com
Note 2780668 - SAP First Guidance - BW NLS Implementation with SAP IQ
Note 3017355 - SAP IQ 16.1 SP04 PLx - correct SAPIQDB.cfg settings
Note 3094594 - Enable SAP IQ for DBACOCKPIT
Note 3085053 - Landscape Considerations for NLS and ILM
Note 3119008 - Configure SAP IQ and HANA for SDA/ODBC
Note 3196447 - troubleshooting an existing SAP IQ 16.x Implementation for NLS/DTO and ILM

Imagine now, there is a simple routine based on the SAPHostAgent to install SAP (Sybase) IQ 16.x with three input values to create any kind of SAP IQ database instantly:

<SID> (What, also IQ gets a SAP <SID>?)

Instance Number (what's about port 2638?)

Size of DB in GB (including all sizing considerations)

even more, to solve all mysteries around the usage there is the following source available:

Blog - demystifying NLS/ILM error messages …

enable HTTPS Port 1129 for the SAP Host Agent

either you call the SAPHostAgent via HTTP Port 1128 or HTTPS Port 1129
SAP Help - Configuring SSL for SAP Host Agent on UNIX

or run the following command (same setup as for the SL Container Bridge)
check that NO secondary sapadm user is logged on to the server via ADS!

server:/usr/sap/hostctrl/exe # ./saphostexec -install
# optional TLS 1.2 since PL57 for 7.22
server:/usr/sap/hostctrl/exe # ./saphostexec -setup tls12
# check the correct settings for /tmp
server:/usr/sap/hostctrl/exe # mount | grep "/tmp"
/dev/mapper/vg_sys_r1-tmp on /tmp type ext4 (rw,nodev,noexec,noatime,data=ordered)
server:/usr/sap/hostctrl/exe #
# check the correct execution of the SAPHostAgent webmethods (*.conf)
server:/usr/sap/hostctrl/exe # saphostctrl -host <hostname> -function ExecuteOperation -name versioninfo
Webmethod returned successfully
Operation ID: 5CF3FCDCB65C1EDD9281CCF3DEC085A5
Info: Retrieve version info: Linux
Info: saphostcontrol: exitcode=0
Info: saphostcontrol: 'Retrieve version info' successfully executed
server:/usr/sap/hostctrl/exe #

add/check the following parameter to the file host_profile and restart the SAPHostAgent

server:/usr/sap/hostctrl/exe # vi host_profile
service/admin_users = sapadm <sid>adm
service/disable_capability = ExecuteOperation/PermissionPolicy
saphostagent/ssl_setup = true
ssl/server_pse = /usr/sap/hostctrl/exe/sec/SAPSSLS.pse
server:/usr/sap/hostctrl/exe # ./saphostexec -restart

now it is possible to call the SAPHostAgent on HTTPS Port 1129

#create the url on host via:
echo https://$(hostname -f):1129/NLSSetup/prepare

If you still have trouble with the execution see the - SAP Host Agent Troubleshooting Guide

the Answer: Q - the easy Installer for SAP IQ

Q introduces to James ...

the following picture shows the Setup of - Q - which is actually the same Installation Procedure as the SAP Datahub SL Container Bridge Architecture ...

Q - overall process of functionality

Version v0.75(s) uses "standard" values for IQ Block and Page Size
(you can further optimize the Block and Page size for the ILM and NLS use case. (s) stands for SSL/TLS setup of the SAP IQ database)

Q - enter the three values

SAP IQ Hardware Sizing Guide – The Complete Memory Picture

Note 2551457 – SAP IQ 16.1 SP 03 Supported Operating Systems and Versions
Note 2669590 - SAP IQ 16.1 SP 04 Supported Operating Systems and Versions
Note 3060790 - SAP IQ 16.1 SP 05 Supported Operating Systems and Versions

Please Note: IQ 16.1 SP05 PL12 (the latest Version in 2024)
for SAP NLS and ILM scenarios, also update the IQ Network Client to the latest Version.

Note 3233538 - SAP IQ 16.1 SP04 PL14 (Build 17091) Release Notes Information
Note 3233571 - SAP IQNC 16.1 SP04 PL14 (Build 17091) Release Notes Information

Note 3444872 - SAP IQ 16.1 SP05 PL12 (Build 14315) Release Notes Information
Note 3444902 - SAP IQNC 16.1 SP05 PL12 (Build 14315) Release Notes Information

In case the /tmp Directory is mounted as read-only then follow the steps in the Note
Note 2104632 - ./setup.bin raises warning /tmp does not have enough disk space! - SAP IQ
Note 3218186 – Error: Create new (IQ) database from utility database fails

# in case for a manual IQ binary Installation or upgrade
server:/ebf29137 # export IATEMPDIR=/sapmnt/tmp
server:/ebf29137 # export DISPLAY= 10.123.456.789:0.0
server:/ebf29137 # ./setup.bin
Preparing to install
Extracting the JRE from the installer archive...
Unpacking the JRE...
Extracting the installation resources from the installer archive...
Configuring the installer for this system's environment...
Launching installer...

The SAPHostAgent uses a routine called "sapinstancebuilder" which creates a complete file system structure equally to an existing SAP Systems like SAP HANA or SAP BW/ERP/etc.

the SAP Instance Builder function in the SAP Host Agent

You can always watch the progress of the IQ Installation in the URL of the Browser
http://server.domain.ext:1128/NLSSetup/prepare
https://server.domain.ext:1129/NLSSetup/execute

/usr/sap/watch -d du -sh <SID>/*
/sapmnt/watch -d du -sh <SID>/*

or alternatively in the SAPHostAgent working directory

/usr/sap/hostctrl/work # tail -f nls.execution.status

Details of the Q Logfile

create DSN (.odbc.ini) with iqdsn

Once the IQ Database Installation is finished, you can use the I-SQL Windows Client to connect to the Database and Check the status with the sp procedures.

connect to the SAP IQ database with the I-SQL Client

SAP IQ allows you with the sql tool iqdsn to create a DSN entry directly in the file .odbc.ini located in the home directory of the SAP IQ Administrator.
Note 2285881 - How to Enable ODBC Connection Tracing in SAP IQ

sq1adm@hostname:/usr/sap/SQ1/data/db> iqdsn -y -w iq_utility -c "UID=DBA;PWD=Sybase4me;
ServerName=iq_utility;DatabaseName=utility_db;HOST=${HOSTNAME}:34238"
sq1adm@hostname:/usr/sap/SQ1/data/db>
iqdsn -y -w hostname_SQ1_11 -c "UID=DBA;PWD=Sybase4me;DatabaseName=SAPIQDB;
ServerName=hostname_SQ1_11;HOST=${HOSTNAME}:$SIQ_SERVERPORT"
Configuration "hostname_SQ1_11" written to file /usr/sap/SQ1/home/.odbc.ini
sq1adm@hostname:/usr/sap/SQ1/data/db> dbping -d -c dsn=hostname_SQ1_11
Connected to SQL Anywhere 16.1.50.1949 server
Ping database successful.
sq1adm@hostname:/usr/sap/SQ1/data/db> iqdsn -l -b
SAP IQ Data Source Utility Version 16.1.50.1949
SAP IQ User Data Sources:
iq_utility
hostname_SQ1_11
sq1adm@hostname:/usr/sap/SQ1/data/db>

essential sp procedures to check the SAP IQ database

# essential sp procedures for SAP IQ
sq1adm@hostname:/usr/sap/SQ1/data/db>
dbisql -c "uid=SAPSQ1USR;pwd=Sybase4me;DSN=hostname_SQ1_11;logfile=connect_SQ1.txt" -nogui
(SAPSQ1USR>)
sp_iqlmconfig;
sp_iqstatus;
sp_iqcheckoptions;
sp_displayroles;
sp_iqdbspace;
sp_iqdbspaceobjectinfo USER0001;

optional SAP IQ database validation procedure

sq1adm@hostname:/usr/sap/SQ1/data/db> dbvalid -c "uid=DBA;pwd=Sybase4me;DSN=hostname_SQ1_11"
SQL Anywhere Validation Utility Version 17.0.9.4899
WARNING: database being validated was not started read-only
VALIDATE DATABASE
VALIDATE TABLE "SYS"."DUMMY"
...

VALIDATE TABLE "rs_systabgroup"."rs_lastcommit"
VALIDATE TABLE "rs_systabgroup"."rs_threads"
No errors reported
sq1adm@hostname:/usr/sap/SQ1/data/db>

If for any reason it is not possible to connect to the IQ Database with the Windows Client, you can also directly call the Interface with the tool dbisql (also with the usage of the DSN above)

Blog - Increasing the SAP-NLS Performance

prepare the SSL/TLS IQ Setup

if you want to enable an encrypted SAP IQ Database with SSL/TLS support, it is suitable to include the following software components to the SAP IQ installation environment:

SAP Host Agent 7.22

SapMachine 11 or higher

openssl V3.x

if you want to install a newer version of openssl to the existing OS Version do as follows:

### Unix / Linux / macOS - download from https://www.openssl.org/source/

/opt/openssl/> tar -xvf openssl-3.0.4.tar.gz
/opt/openssl/openssl-3.0.4> ./Configure
/opt/openssl/openssl-3.0.4> make
/opt/openssl/openssl-3.0.4> make install
/opt/openssl/openssl-3.0.4> ldconfig -v
/opt/openssl/openssl-3.0.4> openssl version -v
OpenSSL 3.0.4 21 Jun 2022 (Library: OpenSSL 3.0.4 21 Jun 2022)

prepare the SSL scenario

Note 3093731 - How to set up TLS (Transport Layer Security) in SAP IQ16.X -- SAP IQ
Note 2117279 - SAP IQ: How to generate CSR for third party trusted CA (VeriSign)

Q - the easy SAP IQ Database Installer is creating this now automatically.

sq1adm@hostname:/usr/sap/SQ1/data/db>

createcert -x -ca 1 -co rsaroot.crt -ko rsaroot.key -kp changeit -io dummy -scn "IQ Certification Authority" -so "IQ, Inc." -sou "IQ Signing Department" -sl "Walldorf" -sst "ON" -sc "CA" -3des -v 10 -u 6,7 -b 2048 -m "101"

createcert -ca 0 -co dummy1 -ko dummy2 -kp changeit -io rsaroot.id -c rsaroot.crt -ck rsaroot.key -cp changeit -scn "hostname.fqdn" -so "IQ Inc." -sou "IQ Accounts" -sl "Walldorf" -sst "ON" -sc "CA" -3des -v 10 -u 1,3 -b 2048 -m "102"

sq1adm@hostname:/usr/sap/SQ1/data/db> dir
-rwxrwxr-x 1 sq1adm sapsys    1436 Jul 26 13:47 rsaroot.crt
-rwxrwxr-x 1 sq1adm sapsys    4729 Jul 26 13:47 rsaroot.id
-rwxrwxr-x 1 sq1adm sapsys    1874 Jul 26 13:47 rsaroot.key
sq1adm@hostname:/usr/sap/SQ1/data/db>

or use the Tutorial - Set Up Transport Layer Security (TLS) in SAP IQ

SSL logon with I-SQL to IQ

SSL encrypted remote source IQ - HANA

connect to a SSL/TLS encrypted SAP IQ Database via dbisql has to be enhanced:

dbisql -c "uid=DBA;pwd=Sybase4me;eng=hostname_<SID_<nr>;LINKS=TCPIP;ENC=TLS(FIPS=Y;TLS_TYPE=RSA;trusted_certificate=hostname_<SID>_<nr>.crt;skip_certificate_name_check=on)" -nogui

the SAP IQ Remote Source had to be enhanced as well (Extra Adapter Properties):

EngineName=hostname_<SID>_<nr>;DatabaseName=SAPIQDB;CommLinks=tcpip(host=hostname.fqdn;port=3<nr>38);LINKS=TCPIP;ENC=TLS(FIPS=Y;TLS_TYPE=RSA;trusted_certificates=/usr/sap/B4H/SYS/global/hostnam_<SID>_<nr>.crt;skip_certificate_name_check=on)

the DBCO String is also enhanced for the SSL/TLS usage:

SYBASE_SERVER=hostname.fqdn SYBASE_PORT=3<nr>38 SYBASE_IQ_ENGINE=hostname_<SID>_<nr>
SYBASE_DBNAME=SAPIQDB SYBASE_SSL=1 skip_certificate_name_check=yes
SYBASE_IQ_LOAD_DIR=/sapmnt/IQ SYBASE_CONTYPE=IQ SYBASE_IQ_LOAD=1 SIQ_BULK_OP=1
SYBASE_IQ_CESU-8=1 SYBASE_IQ_LOCKWAIT=600 SYBASE_IQ_BUFFER_SIZE=500000

as this exeeds the limit of 256 CHAR in SAP NetWeaver Versions until 7.50, it is almost not possible the activate the SSL/TLS option in lower Systems due to the limit in the Table DBCON.

the new DBACOCKPIT for IQ

As Flash is no longer available from Adobe since end of 2020, the SAP IQ COCKPIT cannot be used anymore as of now. If your Company is not Blocking Flash System Wide, simply use the Waterfox Browser to continue.

You can use the improved and extended IQ Monitoring with Transaction DBACOCKPIT.

Blog - enable SAP IQ for DBACOCKPIT

Note 3223681 – New features for DBACOCKPIT on SAP IQ (cumulative Release)
contains the TCI for SAP_BASIS releases 750 SP11-SP25 and 753 SP00-SP09
for SAP Basis 7.56 (BW/4 2021) the new DBACOCKPIT for IQ is part of the SAP Basis 7.56 SP03

DBACOCKPIT - create/trigger IQ events

DBACOCKPIT - check the IQ license

DBACOCKPIT - check the IQ config file

the SAP BW(/4) nearline storage configuration

Blog - SAP-NLS Solution for SAP BW based on SAP IQ
For the SAP-NLS Implementation finally, you must call some transactions in the SAP BW(/4) backend:

DBCO => depending on the scenario additional work for SDA is necessary
RSDANLCON => create the NLS storage target
DBACOCKPIT => reuse the DBCO connection to monitor the Database within BW(/4)
RSOADSODTO => cold store usage with BW/4

the SAP S/4 ILM Store configuration

Blog - SAP ILM based on SAP IQ Database
For the SAP ILM Implementation finally, you have to call some transactions in the SAP S/4 backend:

SICF => configure Web Services
ILMSTOREADM => ILM Store Administration
WEBGUI => for Web Dynpro only Functions, like SFW5 or AS_AFB
SARA => Archive Administration
SARI => Archive Information System

The needed files can be found in the Document -
SAP First Guidance - Q - the easy SAP IQ Database Installer

please contact roland.kramer@first-guidance.com for a copy of the Document
a recording introducing Q (Q-CustomerCoffeeCorner.mp4)
an example logfile - nls.execution.status

Roland Kramer
@SAPFirstGuidance

“I have no special talent; I am only passionately curious.”

Best-practice key figures for retail core processes

2024-09-20T12:47:53.620000+02:00

The important role of key figures for companies

Key figures are far more than just simple numbers that quantify the success or failure of a company. In the complex and dynamic world of modern business, they are essential for understanding business performance and ensuring financial stability. Especially for retail companies operating in a highly competitive market, KPIs are invaluable. Not only do they provide insight into a company's financial position, but they also serve as indispensable tools for strategic planning, risk management and decision-making at all levels of management.

In detail, these metrics enable companies to gain deeper insights into their business processes. They help to maximize profitability by uncovering weak points and identifying potential cost savings. At the same time, they enable you in better understanding of customer behavior, which can be a decisive competitive advantage, especially in the retail sector. The ability to interpret this data correctly and translate it into concrete measures can make the difference between success and failure in a highly competitive market.

The selection and application of the right key figures is of crucial importance. It is not just about collecting data, but also analyzing and using it in the right context. This is the only way to make well-founded decisions that stabilize the company in the short term and keep it on course for success in the long term. In the following, we will present a detailed overview of the most important key figures along the various process steps that are essential for sustainable and profitable corporate management.

Key figures in retail: analysis of stationary vs. online retail

In today's digital era, we are witnessing a profound transformation of the retail landscape that goes far beyond the way products are sold. Both department stores and online retail have their own unique challenges and opportunities that are reflected in specific metrics. These metrics are not only indicators of success in each of these two sectors, but also indispensable tools for developing and adapting business strategies that are tailored to the respective market requirements.

In stationary retail, where the focus is on the physical shopping experience, key figures such as space productivity, customer frequency and the conversion rate are of central importance. Floor space productivity indicates how much revenue is generated per square meter of retail space and is a key factor in the profitability of a store. Customer frequency, i.e. the number of visitors who enter the store, provides important insights into how well a location is performing and how effectively marketing campaigns are reaching the target group. Finally, the conversion rate shows what percentage of these visitors make a purchase, which allows conclusions to be drawn about the product range and sales strategy.

On the other side of the spectrum is the online retail, which is characterized by completely different dynamics. Here, key figures such as the bounce rate, the average shopping basket value and the repurchase rate play a prominent role. The bounce rate shows how many visitors leave a website after viewing a single page, which can indicate problems with the user-friendliness or relevance of the offer. The average basket value provides information on how much a customer spends on average when making a purchase, which is an important indicator of the effectiveness of cross-selling and upselling strategies. The rate of repurchase, on the other hand, measures customer loyalty and is crucial for the long-term success of an online retailer.

Another key difference is the way customer loyalty and experience are measured. In department stores, factors such as personal advice and the design of the sales area play an important role in customer satisfaction and loyalty, while in online retail, aspects such as website usability, loading times and the checkout process are crucial. These different focal points are also reflected in the respective key figures and require an adapted approach for each case.

To develop a successful omnichannel strategy that seamlessly links both sales channels, it is essential to understand and harmonize the specific key figures of both worlds. Only through a holistic analysis and a deep understanding of the differences and similarities can companies operate successfully in today's highly fragmented retail landscape and remain competitive in the long term. In the following, we will take a detailed look at the most important key figures of both forms of retail and examine their implications for management.

Insert AI Technology: Machine Learning with ABAP Managed Database Procedure for merchandise management

Optimizing the allocation of goods at the point of sale (POS) has the potential to significantly increase the sales rate. The aim of this measure is to avoid empty stock by distributing goods in line with demand. Not only current stock and sales data, but also planning data is considered in order to ensure precise allocation.

In A/B tests, a potential increase in sales of 11 million euros per year was achieved through a 2% higher sales rate. These impressive results illustrate the significant impact that improved merchandise distribution can have on commercial success.

The technical implementation of this optimization takes place in an SAP BW/HANA system, whereby existing data is used for analysis and training. In particular, the integration of the SAP HANA Predictive Analytics Library and the Automated Predictive Library plays a central role here. These libraries enable a well-founded prediction of requirements and precise planning.

It only takes 12 minutes to calculate the optimum merchandise allocation for around 7,000 points of sale. The results are then transferred to the ERP system, where they are made available for merchandise and assortment planning. The shortened calculation time also enables simulations to be carried out, giving companies the flexibility to run through various scenarios and thus determine the best possible allocation.

This holistic and data-driven approach enables companies not only to optimize their product availability, but also to significantly increase their sales and improve the efficiency of their processes at the same time.

Technological platforms for core retail processes

Data & analytics solutions

Where can I learn how to move from an existing Data Warehouse landscape to SAP Datasphere?

2024-10-02T12:57:50.011000+02:00

I recommend to access our Modernizing Your Data Warehouse Landscape – From SAP BW to SAP Datasphere learning journey.

Overview
In this Learning Journey, you will learn which scenarios exist to leverage current investment and modernize your data warehouse landscape. Your main options are the greenfield approach, the hybrid approach with SAP BW/4HANA or SAP S/4HANA, and the migration and conversion approach to SAP BW bridge. You will learn about use cases, value propositions, and steps for these approaches. The learning journey contains step-by-step procedures and videos explaining how you implement the connections and objects in a real system for each scenario.

Learning objectives
After completing this Learning Journey, you will be able to understand different paths and technological options to move from an existing Data Warehouse landscape based on SAP NetWeaver BW or SAP BW/4HANA to SAP Datasphere and SAP BW bridge.

Prerequisites

Exploring SAP Datasphere
Recommended:
- Modeling in SAP Datasphere, SAP BW bridge
- BW410

Please ask a question related to the digital learning Journey in the Q&A area.

Our SAP Learning Experts will get back to you as soon as possible! We are here to support you.

I appreciate your feedback and we will make sure to continue sharing interesting topics.

Kind regards
Margit

Manhattan WMS integration with SAP S/4HANA

2024-11-05T13:06:52.848000+01:00

Manhattan WMS integration with SAP S/4HANA

In the complex world of modern supply chains, businesses often use a combination of software systems to manage various processes. One common scenario involves integrating the Manhattan Warehouse Management System (WMS) with SAP S/4HANA. This integration allows businesses to leverage the strengths of both systems and achieve seamless warehouse operations across their supply chains.

Here’s an in-depth look at the integration process, benefits, and key considerations when integrating Manhattan WMS with SAP S/4HANA.

Overview of Manhattan WMS and SAP S/4HANA

Manhattan WMS:

Manhattan Associates offers a comprehensive Warehouse Management System (WMS) that supports advanced distribution and logistics management. Known for its flexibility and scalability, the system provides tools for labour management, inventory visibility, and slotting optimization.

Manhattan WMS is a highly scalable, feature-rich solution used by businesses worldwide to manage and optimize their warehouse operations. It is recognized for its advanced capabilities in picking, packing, and inventory management, effectively addressing complex warehousing requirements through configurable workflows.

SAP S/4HANA:

SAP S/4HANA is an integrated enterprise resource planning (ERP) system developed by SAP. Built on SAP's in-memory database, HANA, it offers real-time data processing and analytics, streamlining business operations across various functions like finance, supply chain, sales, and procurement. Its core features include simplified data models, enhanced performance, and cloud, on-premise, or hybrid deployment options. S/4HANA is designed to help businesses transition to digital, enabling them to be more agile, efficient, and data-driven.

Both systems offer distinct advantages, prompting businesses to consider integration to harmonize overall supply chain and logistics operations.

Why Integrate Manhattan WMS with SAP S/4HANA HANA?

Integrating Manhattan WMS with SAP S/4HANA offers several benefits, especially for businesses with diverse warehouse management needs. Here are some key reasons for integration:

a) Leverage Specialized Functionalities:

Manhattan WMS is known for its robust warehouse management features, while SAP S/4HANA excels at integrating warehouse operations with broader business processes, such as finance and procurement. Integrating both systems allow businesses to utilize the strengths of each solution.

b) Unified Data Flow:

Integration ensures seamless data flow between Manhattan WMS and SAP’s ecosystem, allowing real-time updates on inventory, order fulfilment, and stock movements. This creates a more synchronized supply chain, minimizing errors, and improving visibility.

c) Enhance Scalability and Flexibility:

Manhattan WMS manages highly complex warehouse operations, while SAP S/4HANA ensures integration with ERP systems. Together, they allow businesses to scale their warehouse operations across multiple regions or distribution centers without sacrificing efficiency.

d) Support for Complex Warehousing Models:

For businesses operating multiple warehouses or using third-party logistics providers, integrating Manhattan WMS and SAP S/4HANA helps maintain unified and consistent operations.

Integration Methods: Key Approaches

Integrating Manhattan WMS with SAP S/4HANA requires proper planning and leveraging either standard or custom integration methods. Here are the common approaches to integration:

a) Application Programming Interfaces (APIs):

APIs provide a flexible and scalable method to exchange data between Manhattan WMS and SAP S/4HANA. Many modern WMS solutions, including Manhattan, offer APIs to connect with ERP and other systems. SAP S/4HANA also supports APIs for smooth communication with external systems.

How it works:

Data from Manhattan WMS, such as order status, stock levels, or shipment details, is sent via APIs to SAP S/4HANA.
SAP S/4HANA processes this data and sends relevant updates back to Manhattan WMS through the API channels.

b) Middleware Integration:

A middleware solution (such as SAP PI/PO or a third-party integration platform) acts as a bridge between the two systems. The middleware transforms and transfers data between Manhattan WMS and SAP S/4HANA, ensuring compatibility and real-time synchronization.

Advantages:

Centralized data mapping and transformation.
Supports integration with other business systems, such as CRM or transportation management.

c) Direct Database Integration:

In some cases, businesses can integrate the databases of Manhattan WMS and SAP S/4HANA directly. While this approach requires advanced customization and technical expertise, it enables real-time data sharing.

Considerations:

Custom development might be needed.
Data integrity and compatibility checks are essential to avoid inconsistencies.

d) File-Based Integration (IDoc/BAPI):

SAP S/4HANA can integrate with external systems using IDocs (Intermediate Documents) or BAPI (Business Application Programming Interface) to send and receive messages. This method is often used for exchanging bulk data such as orders, inventory movements, and stock status updates.

Process:

Manhattan WMS sends data in structured file formats, such as XML or CSV, which is then converted into IDoc or BAPI format and processed by SAP S/4HANA.
Similarly, SAP S/4HANA can generate files and send them to Manhattan WMS to update warehouse operations.

Key Data Flows in Manhattan WMS and SAP S/4HANA Integration

Integrating Manhattan WMS with SAP S/4HANA involves the seamless exchange of critical data to ensure efficient warehouse operations. Some of the key data flows between Manhattan WMS and SAP S/4HANA during integration include:

Order Management:

Data related to order creation, picking, packing, and shipping is shared between both systems to ensure accurate order fulfilment and inventory synchronization.

Inventory Management:

Inventory movements, stock counts, and replenishments are updated in real-time, ensuring both SAP ERP and Manhattan WMS always reflect the same stock levels.

Warehouse Tasks:

Tasks like picking, packing, and putaway are optimized through real-time data synchronization, enhanced inventory visibility, and efficient task management.

Integration between Manhattan Warehouse Management System (WMS) and SAP S/4HANA requires the seamless exchange of key data flows to ensure efficient warehouse operations. Below are some of the most important data flows between the two systems, focusing on key processes like inbound and outbound logistics, inventory management, and more.

Outbound Process/Order Fulfilment

1. Master Data Synchronization

Products/Materials: Master data related to products, materials, and stock keeping units (SKUs) must be synchronized between Manhattan WMS and SAP S/4HANA to ensure consistency. This includes attributes such as weight, dimensions, product category, and handling instructions.
Business Partners: Data for business partners like customers, suppliers, and carriers must be shared across both systems for streamlined inbound and outbound logistics.
Warehouse Structure: Layouts such as storage bins, storage types, and areas need to be consistent between the two systems.

2. Inventory Data Flow

Inventory Balances: Real-time updates of stock levels in different locations or bins must be synchronized between Manhattan WMS and SAP S/4HANA.
Stock Adjustments: Any changes in stock due to damages, cycle counts, or other adjustments must be updated and communicated between both systems.
Inventory Transfers: Movement of goods between different warehouse locations should trigger the update of inventory levels in both systems.

3. Inbound Process (Goods Receipt)

Advanced Shipping Notices (ASN): When inbound deliveries or shipments are received, ASNs from SAP S/4HANA may be sent to Manhattan WMS to trigger the receipt process.
Goods Receipt Confirmation: Once goods are received and checked in Manhattan WMS, confirmation is sent to SAP S/4HANA to update inventory levels and trigger subsequent processes like quality inspections or putaway.
Putaway Instructions: Manhattan WMS may handle putaway instructions based on SAP S/4HANA data, such as predefined storage bins.

4. Outbound Process (Order Fulfilment)

Sales Orders/Outbound Deliveries: Sales orders created in SAP are transferred to Manhattan WMS, which handles picking, packing, and shipping processes.
Pick Confirmation: Once the order is picked, a confirmation is sent back to SAP S/4HANA, which updates the stock level and outbound delivery status.
Shipping Confirmation: Manhattan WMS communicates shipping confirmations back to SAP S/4HANA, which triggers invoicing and shipment tracking.

5. Returns Handling

Returns Order Creation: SAP S/4HANA or SAP ERP may generate a returns order, which is passed to Manhattan WMS for processing the physical return.
Goods Receipt for Return: Returned goods are processed in Manhattan WMS, and the information is sent back to SAP S/4HANA to update the inventory and returns status.

6. Transportation Data Flow

Shipping Instructions: Shipping or transportation orders generated in SAP S/4HANA are sent to Manhattan WMS for shipment planning and execution.
Carrier Data: Information about the carriers, tracking numbers, and shipping labels is updated in SAP S/4HANA once the shipment is confirmed in Manhattan WMS.

7. Handling Unit Management

HU Data Synchronization: Handling unit data such as pallets, cartons, etc. and their statuses must be consistently updated between both systems, especially during outbound and inbound processes.

8. Reporting and Analytics

Operational Data: Manhattan WMS sends operational data such as order status, inventory levels, and performance metrics to SAP S/4HANA for analysis and reporting.
Warehouse Performance: KPIs like order accuracy, pick rates, and stock levels are exchanged to create comprehensive reports.

9. Error Handling and Reconciliation

Error Logging: Any errors in data transfer (e.g., stock mismatches, failed transactions) between the two systems should trigger error logs for reconciliation.
Reconciliation Reports: Data reconciliation ensures that inventory levels, order statuses, and other critical data match between Manhattan WMS and SAP S/4HANA.

Integration Technologies

Middleware/Integration Platform: Middleware platforms (e.g., SAP PI/PO, MuleSoft) or API-based integration are commonly used to manage data flows between Manhattan WMS and SAP S/4HANA.
IDocs/EDI: Standard formats like IDocs, XML, or EDI are used to transfer data related to orders, shipments, and inventory between systems.

Summary of Key Data Flows:

Master Data: Products, partners, warehouse structure.
Inventory Data: Stock levels, adjustments, and transfers.
Inbound Logistics: ASN, goods receipt, putaway.
Outbound Logistics: Orders, picking, shipping confirmation.
Returns: Return orders and stock updates.
Transportation: Shipping instructions and carrier updates.
Handling Units: Handling unit updates and synchronization.
Reporting: Operational metrics and performance data.
Error Handling: Reconciliation and error logging.

These data flows ensure smooth warehouse operations, inventory accuracy, and alignment of order fulfilment processes across both systems.

Communication Ports and Protocols:

Integration between SAP S/4HANA and Manhattan Warehouse Management System (WMS) typically involves communication via middleware or direct API calls over specific communication ports. Common communication protocols include:

SAP S/4HANA often uses standard ports for communication over protocols like HTTP(S), SOAP, REST, IDoc, or RFC for message exchanges.
Manhattan WMS may have different integration approaches, but it commonly relies on APIs (SOAP/REST) or message queues like JMS for communication.

Some commonly used ports:

HTTP (80) / HTTPS (443):
- Used for REST and SOAP API calls for communication between Manhattan WMS and SAP S/4HANA via web services.
RFC (SAP-specific, typically over ports 3300-3399):
- SAP S/4HANA uses Remote Function Calls (RFC) for external system integration. Middleware may be required to translate these to compatible API calls for Manhattan WMS.
IDoc (Intermediate Document, typically over ports like 3300-3399 for RFC):
- SAP S/4HANA may send or receive IDocs to/from Manhattan WMS. Ports used are typically similar to RFC communication.
JMS (Java Message Service):
- If Manhattan WMS uses a message queue, SAP S/4HANA might need to communicate over a JMS queue using middleware like IBM MQ or TIBCO. The port could vary based on the specific queue implementation, often 61616 for ActiveMQ.
FTP/SFTP (Ports 21 for FTP, 22 for SFTP):
- File-based data exchange between SAP S/4HANA and Manhattan WMS can be done via FTP/SFTP.

Example Integration:

Middleware platforms like SAP PI/PO, Dell Boomi, or Mulesoft are often used to translate between SAP S/4HANA formats (IDoc, BAPI, RFC) and the APIs or message formats used by Manhattan WMS.
Both systems can communicate by exchanging data over a predefined integration protocol like XML, JSON, or flat file.

Specific Ports to Configure:

SAP Gateway/Dispatcher: Ports 3300+Instance Number for RFC communication.
Web services/HTTP: Ports 80/443 for web-based communication (SOAP/REST).
FTP/SFTP: Ports 21/22 for file transfers.

To determine the exact ports, the middleware or direct integration protocol needs to be evaluated, and port configurations should align with security policies and firewall settings of both systems.

Real-World Example: A Success Story

A global retail giant successfully integrated Manhattan WMS with SAP S/4HANA to optimize operations across distribution centers in multiple regions. The integration enabled real-time inventory updates, reduced order processing times, and improved labour productivity. By using middleware for seamless communication between the systems, they were able to handle large volumes of data without compromising operational accuracy.

Conclusion

Integrating Manhattan WMS with SAP S/4HANA provides businesses with a powerful solution that enhances warehouse efficiency, data accuracy, and end-to-end supply chain visibility. However, to ensure success, businesses must carefully plan, adopt best practices, and address potential challenges head-on. Embracing this integration can drive efficiency, minimize errors, and enable growth in an increasingly competitive landscape.

Connectivity-BW4HANA & SAC:: Configuration of Import Connection

2024-12-04T11:49:26.529000+01:00

Integration between SAC and BW offers a dynamic and comprehensive solution for accessing, analyzing, and visualizing data stored in BW.

Types Of Connections:

1.) Import: When there is a need to bring data from BW into SAP SAC for further analysis, modeling, and visualization, an import connection between SAP SAC and BW is necessary. Below we will explore the step-by-step process of configuring an import connection and setting it up in SAP SAC.

2.) Live: A live connection between SAP SAC and BW is preferred when real-time access to BW data is critical for decision-making. Below we will explore the step-by-step process of configuring a live connection and setting it up in SAP SAC.

Configuration: Import Connection between BW4HANA & SAC:

To configure the data source in SAP SAC, follow these simple steps.

1.First, navigate to the “system” tab in the administration section. Next, click on the “data source configuration” tab, where you will encounter a variety of data sources to choose from. Scroll down until you find the SAP BTP core account option. Take note of the region host and sub-account user details displayed there, as they will be essential for the next step.

2.To establish a secure connection between your on-premise application and the cloud, a cloud connector is essential. This crucial tool acts as a handshaking application, facilitating a seamless link between the two environments. Before proceeding with the installation of the cloud connector, ensure that you have a SAP JVM ready. Locate or download the SAP JVM and make note of its file path, as you will need to specify this during the installation process.

3.Once you have the SAP JVM, extract it onto the server where you plan to install the Cloud Connector. As an example, you can unzip the file into the directory C:\SAP JVM\sapjvm_8. You can download it from this link: https://tools.hana.ondemand.com/#cloud

4.After obtaining the SAP JVM from the provided link, download the cloud connector from the same source. During installation, specify the folder location of the SAP JVM.

5.Once the installation process is complete, open your web browser and enter the following URL: https://localhost:8443. This will take you to the logon screen. Here, use the default credentials: “Administrator” for the username and “manage” for the password. It is recommended to change the password after your initial logon. At this point, you will be prompted to choose between a master and shadow installation. Opt for the “Master” option if you are installing a single Cloud Connector instance or the primary instance from a pair of Cloud Connector instances.

6.Next, enter the subaccount details that you noted earlier. Specify proxy host and port if using a proxy, otherwise leave blank. Optional to enter Location ID for multiple Cloud Connectors. Remember to save the configuration.

7.Once you have completed the necessary configuration, click on the “Connect” button. After doing so, verify that the connector state is displayed as “Connected.” This confirmation ensures that the Cloud Connector has successfully established.

8.When establishing connections with SAP BPC MS, SAP BW, SAP UNX, or SAP ERP systems, it is necessary to install the SAP Analytics Cloud agent in addition to configuring the Cloud Connector. However, for connections with SAP BPC NW, SAP BPC for BW/4HANA, OData, or SAP S/4HANA, configuring the Cloud Connector alone is sufficient.

9.To proceed with the installation of the SAP Analytics Cloud Agent, there is a preliminary step to follow. It involves downloading Apache Tomcat, a widely-used web server and servlet container, from the official website at https://tomcat.apache.org/download-80.cgi. Once the download is complete, simply double click on the Tomcat executable file to initiate the installation procedure. During this step, you will be prompted to accept the license agreement, an important formality. Moving forward, when presented with the Choose Components screen, it is recommended to stick with the default options, which are already configured optimally. And click next.

10.you need to specify the ports to be used by Apache Tomcat. It is essential to ensure that there are no conflicts with existing applications already running on your system.

11.Next, specify the path to your and install.

12.Once you have successfully installed Apache Tomcat, the next vital step in optimizing the performance of SAP Analytics Cloud Agent is to configure the JAVA Heap space allocation. Launching the Tomcat configuration allows you to modify these settings and allocate more memory accordingly. To initiate the configuration process, navigate to the Tomcat installation directory and locate the Tomcat8w.exe file. Simply double click on it to open the configuration window. By default, the initial and maximum heap space values (-Xms and -Xmx) are usually set to 128MB and 256MB respectively, which often prove insufficient for efficient data acquisition in SAC, leading to timeout errors. It is crucial to increase these values to prevent such issues. For enhanced performance, it is recommended to set the values to 1024MB and 2048MB respectively.

13.To install the SAP SAC (Analytics Cloud) agent, begin by downloading it from the SAP Support Portal: https://support.sap.com/swdc. Access the SAP Software Downloads page and navigate to “By Category.” Choose “SAP Cloud Solutions” and then select “SAP ANALYTICS CLOUD CONN SAP ANALYTICS CLOUD CONN 1.0 SAP ANALYTICS CLOUD AGENT 1.0.” Proceed to download the latest version available.

14.Once downloaded, unzip the file and rename the WAR file to C4A_AGENT.war. Extract the package and copy the C4A_AGENT.war file to the webapps directory in your Tomcat installation. When you restart Tomcat, the agent will automatically deploy.

15.To establish the authentication credentials needed for configuring the SAC Agent, follow these steps. First, locate the Tomcat conf directory and open the tomcat-users.xml file. Inside this file, you can add a new user with the Services role. This user will have the necessary permissions to ensure smooth communication between SAP Analytics Cloud and the SAC Agent. Simply provide a unique username and password for the new user, and make sure to save the changes.

16.For establishing a connection with SAP BW, an additional component called the SAP JCO connector is required. To obtain it, you can download the connector from the official SAP website at http://support.sap.com/swdc. Once the download is complete, extract the contents of the downloaded zip file. Within the extracted files, locate the sapjco3.dll and sapjco3.jar files. Copy these two files and paste them into the tomcat/lib directory. After completing the file transfer, it is essential to restart the Tomcat application for the changes to take effect.

17.Now go to your cloud connector at http://localhost:8443. Enter your login credentials to access the Cloud Connector administration console. Once logged in, select the “Cloud To On-Premise” option. Next, locate the “+” icon and click on it to add a new Access Control entry. This step is crucial for defining the access permissions for on-premises systems.

18.The next step is to specify the Back-end Type. Select “Other SAP System” as the Back-end Type from the available options.

19.Then select HTTP protocol. Choose HTTPS if SSL configured on the Tomcat instance where the BOC Agent is deployed.

20.enter the hostname (Internal Host) and port number (Internal Port) of the Tomcat server where the SAC Agent is running. The default HTTP port for Tomcat is 8080, while the default HTTPS port is 8443. Therefore, when specifying the Internal Host, provide the hostname or IP address of the Tomcat server. For the Internal Port, enter the corresponding port number based on whether you are using HTTP or HTTPS (8080 for HTTP or 8443 for HTTPS).

21.Now give virtual host and port that will be used on SAC side.

22.Select “None” as the Principal Type. This selection indicates that there is no specific principal or user identity associated with the access control settings. By choosing “None,” you are specifying that the access control applies to all users or principals attempting to access the specified resources. This can be useful in scenarios where you want to grant general access to the SAP SAC Agent without requiring specific user authentication or identity verification. However, please note that depending on your specific security requirements, you may need to consider alternative options or additional authentication mechanisms to ensure secure access to the SAC Agent.

23.Select the option “Check availability of internal host” to ensure the HANA Cloud. Connector can access the C4A Agent and click finish

24.The next step is to add a resource in the Cloud Connector configuration. Set the URL PATH to “/C4A_AGENT/” and choose the option “Path and all sub-paths” to allow access to this specific path and its sub-paths. This URL PATH represents the endpoint or route where the SAC Agent is accessible within the Tomcat server. By adding this resource and specifying the URL PATH, you are granting the necessary permissions for SAP SAC (Analytics Cloud) to communicate with the SAC Agent and retrieve the required data. Once the URL PATH and access settings are configured, click on the “Save” button to save the resource configuration.

Creating an import connection in SAC:

Follow these simple steps within your SAC tenant. Firstly, navigate to the administration section and access the data configuration tab. Here, you can establish a connection to your on-premise data sources by adding a new location. Enter the required information to create the connection. Specify the Host as the virtual hostname that you previously defined during the HANA Cloud Connector configuration. Next, input the virtual port that you set during the HANA Cloud Connector configuration in the Port field. In the Username and Password fields, enter the credentials that were specified in the tomcat-users.xml file. These credentials ensure secure authentication for the connection between SAC and your on-premise data sources. Once you have entered all the necessary information, simply click on the Create button. This will establish the connection, allowing you to access and analyze your on-premises data.

Happy Learning!!

Boosting Performance in SAP Analytics Cloud: Simple and Efficient Strategies

2025-01-19T16:56:34.177000+01:00

Objective: Optimize the performance in SAP Analytics Cloud using simple and efficient steps.

I strongly believe data modeling is best optimized in Datasphere rather than HANA modeling. Therefore, it is possible to enhance the performance of dashboards in SAC by following the steps mentioned below:

Follow widget weightage rules by SAP.
Implementing Apply buttons for input control, measure control, and dimension control.
Filter Optimization: Use Story Filters Instead of Page Filters.
Collapse Page Filters and Input Controls

Let breakdown each thing,

Follow Widget Weightage Rules by SAP

Overview: SAP Analytics Cloud assigns widget weightage based on complexity and resource consumption. To ensure optimal performance, developing dashboards while adhering to the widget weightage rule is advisable. If multiple KPIs need to be developed, they should be distributed across separate pages, maintaining a recommended total weight of 5 units per page.
Performance Considerations: Developing 14 to 20 KPIs and multiple input controls on a single page may be optimized for design but could impact performance.
Best Practice: Instead of placing all KPIs on one page, split them according to the widget weightage rule, ensuring no more than 5 units per page. This will help in achieving an optimized, high-performing dashboard.
For better understanding visit: Apply Best Practices for Performance Optimization in Story Design | SAP Help Portal

Optimization Tips:

Use Optimized Design Experiences to create stories.
Limit the number of widgets on a single page.
Go with optimized charts such as bar or line charts instead of complex visualizations where possible.
Minimize the use of widgets with high rendering weights, such as maps or complex hierarchical tables.
Leverage the Performance Analyzer to assess whether the story is optimized and identify any issues. It will display which widgets or scripts take longer to load.

Implement Apply Buttons for Input Control, Measure Control, Dimension Control

Implementing an Apply Button for input, measure, and dimension controls is an effective way to optimize performance in SAP Analytics Cloud (SAC) dashboards.
By using an Apply button, you allow users to make multiple selections or changes without triggering data recalculations or page refreshes with every single input. Instead, the system will only process the changes when the user explicitly clicks the Apply button, minimizing unnecessary data reloads and improving dashboard performance.

Benefits of Using an Apply Button:

Improved Performance: By delaying the data refresh until the user clicks the Apply button, you reduce the load on the system, especially for complex stories with multiple filters.
User Control: Users can select multiple filters or options at once and see the effect of their selections only after applying them, leading to a more controlled and predictable user experience.
Avoid Redundant Queries: Without an Apply button, the system may trigger data queries for every change made to the input controls, which can slow down performance. The Apply button ensures that only the necessary queries are triggered when needed.
In this case, the dashboard triggers a data query every time the user changes a filter without the apply button and with the apply button. Here's an example of how SQL queries would be triggered

Scenario	SQL Queries
Without Apply	Query 1: SELECT * FROM sales_data WHERE Invoice_Date IN ('2020', '2021', '2022') Query 2: SELECT * FROM sales_data WHERE Invoice_Date IN ('2020', '2021', '2022') AND Region IN ('South', 'Southeast')

With Apply	Query (After Apply): SELECT * FROM sales_data WHERE Invoice_Date IN ('2020', '2021', '2022') AND Region IN ('South', 'Southeast')

Apply Button

Impact on Performance:

Without Apply Button: Multiple queries are sent to the data source for each filter change, causing extra load and slowing down the dashboard performance, especially with larger datasets or complex queries.
With Apply Button: Only a single query is triggered after the user applies their filter selections, improving performance by reducing the number of queries and data retrieval times.

Filter Optimization: Use Story Filters Instead of Page Filters

If the same filter/input control is repeated across all pages in a story, use a Story Filter (Global Filter) instead of Page Filters. Here's why:

Consistency Across Pages:
A Story Filter applies the same filter to all pages, ensuring uniformity in data across the story.
Simplified User Interaction:
Users don’t need to set the same filter on every page; changing it once updates all pages.
Reduced Complexity:
Managing one Story Filter is simpler than maintaining multiple Page Filters with identical logic.
Improved Performance for Shared Filters:
Since the filter is applied globally, it reduces redundancy in querying and ensures a smoother user experience.

Usage:
Apply a Story Filter when the same filter (e.g., Region or Date) is required across all pages to maintain a consistent context and improve usability.

Collapse Page Filters and Input Controls

Expanded page filters or input controls are convenient because you can quickly select members or search for members in the control. However, an expanded input control refreshes often and that can affect the story's performance.

When the input control is collapsed, you must select the control to display the member list before you can search or make your selections.

Hope this helps you guys.

Thank you.

Expired Tablets? Not on our Watch!Linking and Tracing value KPI to process PPI to DQ/Analytic/AI KPI

2025-01-22T16:51:05.399000+01:00

Background

In the consumer healthcare world, trust is paramount. Consumers rely on us for products that deliver on their promises, and safety is non-negotiable. But behind every bottle of cough syrup or pack of pain relievers lies a complex web of processes that must work flawlessly.

Behind every bottle of pills lies a data-driven secret to winning market share. In this blog, Let us discover how optimizing batch and shelf-life processes, guided by the right KPIs, can unlock greater efficiency and consumer trust.

Why this process is critical to the consumer health care?

Effective batch management and shelf-life tracking are essential for consumer healthcare companies, leading to:

Increased Product Availability: Ensures products are consistently in stock, both in stores and online, leading to higher purchase frequency and household penetration.
Enhanced Consumer Trust: Demonstrates a commitment to quality and safety, building brand loyalty and encouraging consumers to explore more product categories as there are less product recalls.
Optimized Distribution: Minimizes waste by prioritizing the distribution of products with shorter shelf lives, ensuring fresher products reach consumers.
Improved Consumer Experience: Reduces the risk of consumers receiving expired or ineffective products, leading to higher satisfaction, repeat purchases, and increased household penetration.
Stronger E-commerce Performance: Ensures that products sold online have valid shelf lives and arrive in optimal condition, boosting consumer trust and driving online sales.
Enhanced Brand Reputation & Compliance: Strengthens the brand's reputation for safety and reliability by meeting regulatory standards, attracting and retaining customers.

What Business Value and Process and Analytic KPI's need to be set up for driving process efficiencies?

Ditch the spreadsheets and gut feelings! To build a truly efficient consumer healthcare supply chain, you need KPIs that pack a punch.Tired of expired products eating into your profits? The cure is clear: KPIs that connect the dots between business value, process optimization, and data-driven insights. Get ready to transform your batch management and shelf-life tracking from cost center to competitive edge.

Business Value KPI:

Product Market Volume or Share can be used to measure the business value KPI i.e increase in number of products sold and revenue share.

Process Efficiency KPI(s):

To measure the process effectiveness of batch management and shelf life tracking, you can use the following five KPIs (Key Performance Indicators):

Batch Accuracy Measures the percentage of batches that are correctly recorded and matched with the correct product, production date, and shelf life information. This helps ensure compliance and reduces errors.
Shelf Life Compliance Rate Tracks the percentage of products that are used or shipped before their expiration date. It indicates how well the organization is managing product rotations and preventing the use of expired stock.
Batch Traceability Rate Measures the percentage of batches that can be fully traced back to raw materials, production processes, and final products. High traceability is critical for quality control, recalls, and regulatory compliance.
Inventory Turnover by Batch Indicates how often inventory (organized by batch) is sold or used in a given time period. A high turnover rate suggests efficient batch management, preventing stock from aging or becoming obsolete.
Expired/Out-of-Spec Product Percentage Tracks the percentage of products that are expired, approaching expiration, or outside of defined quality standards. A low percentage indicates effective shelf life tracking and proactive batch management.

Analytic KPI's

You're tracking batch numbers and expiry dates, but are you truly analyzing your shelf-life performance? To move beyond the basics, here are 3 KPIs that reveal hidden insights, optimize inventory, and protect your bottom line.

Shelf-Life Consumption Rate by Channel: This KPI goes beyond the average consumption rate and breaks it down by distribution channel (e.g., online, pharmacy, supermarket). This helps identify channels where products are moving slower and might be at higher risk of expiry, allowing for targeted inventory adjustments.
Days to Expiry at Time of Sale: This measures how close products are to their expiration date when they are sold. Tracking this metric over time and by product/batch can reveal potential issues in your supply chain or distribution network. For example, consistently high numbers might indicate a need to improve stock rotation practices or adjust delivery routes.
Write-Off Cost per Batch: This KPI directly links shelf-life management to financial performance. By analyzing the cost of writing off expired products on a per-batch basis, you can pinpoint problematic products, batches, or even production runs that consistently result in higher losses. This data can then be used to investigate root causes, optimize production processes, or adjust forecasting models.

Data Quality KPI's:

Data holds the key to eliminating expired products from your supply chain. By tracking these 3 DQ KPIs, consumer healthcare companies can make smarter decisions, reduce waste, and boost profitability.

Data Completeness Rate: This KPI tracks the percentage of batch records that contain all required fields (e.g., batch number, production date, expiration date, storage conditions).

Formula: (Number of Complete Batch Records / Total Batch Records) * 100
Goal: A higher completeness rate ensures that accurate, full data is available for decision-making and reduces errors related to missing information that could lead to expired products being shipped.

Data Accuracy Rate :Measures the accuracy of data entered into the system (e.g., correct batch number, correct expiration date, accurate shelf life data). Ensuring data is error-free is key for reliable batch management and expiration tracking.

Formula: (Correctly Entered Data / Total Data Entries) * 100

Goal: A high accuracy rate minimizes costly errors in inventory management and ensures shelf life data is reliable for decision-making.

Real-time Data Availability (Latency) : Tracks how quickly batch data (including shelf life and expiration dates) is updated and available for use across systems in real time.

Formula: Average Time for Data Update from Entry to Availability
Goal: Minimize latency to ensure real-time decisions are made, especially for perishable goods, ensuring that expired products are flagged before distribution.

Batch Analytics Utilization Rate : The percentage of batch-related decisions that rely on advanced analytics, such as predictive analytics for expiration forecasts or automated shelf life recommendations.

Formula: (Number of Analytics-driven Decisions / Total Decisions) * 100
Goal: Increase the use of data analytics for batch forecasting, enabling better stock management, reduced waste, and more efficient shelf life tracking.

AI KPI's

AI isn't magic, it's measurable. Track these KPIs to ensure your AI investments are delivering tangible results.

Shelf-Life Prediction Error: Measures the difference between the AI's predicted shelf life and the actual shelf life of a batch. A lower error rate indicates a more accurate and reliable AI model.
Demand Forecast Accuracy: Tracks how well the AI predicts future demand for products, considering factors like seasonality, promotions, and external trends. Higher accuracy leads to better production planning and reduced waste.
Automated Decision Rate: Measures the percentage of batch-related decisions (e.g., stock allocation, replenishment orders) made automatically by the AI system. A higher rate indicates increased efficiency and reduced manual effort.
False Positive Rate (for Expiry Alerts): Tracks how often the AI incorrectly flags products as nearing expiry. A low false-positive rate is crucial to avoid unnecessary interventions and maintain trust in the system.

How we can achieve the process efficiency?

To achieve the efficiency in batch management and shelf life tracking within an SAP environment, several SAP data objects need to be cleansed and maintained properly. Clean data ensures accurate reporting, effective analytics, and improved decision-making. Below are the key SAP data objects that require cleansing:

1. Material Master (MM)

Key Fields to Cleanse: Material Number: Ensure no duplicate or incorrect material numbers are present.
Basic Data: Verify the accuracy of material descriptions, units of measure, and classification.
Batch Management Indicator: This flag determines whether batch management is activated for a material. Shelf Life Data: Ensure expiration dates, shelf life period, and "Best Before" information are consistent and up to date.
Why it's Important: The material master drives many processes in batch management, including the correct tracking of shelf life and batch-specific information.

2. Batch Master (BM) / Batch Management Data

Key Fields to Cleanse: Batch Number: Ensure that batch numbers are unique, and the proper batch number ranges are defined.
Batch Status: Check that the status of the batch is accurate (e.g., whether it’s blocked, released, or expired).
Batch-Specific Attributes: Data fields related to production date, shelf life, and expiration dates need to be consistently maintained.
Why it's Important: Batch data is directly tied to shelf life management, product traceability, and ensuring that expired products are flagged in the system.

3. Vendor Master (VM)

Key Fields to Cleanse: Vendor Data: Cleanse and verify that supplier details are correctly entered, including lead times and supply conditions.
Contract Data: Ensure consistency in contracts related to batch management, particularly with suppliers offering products with specific shelf life requirements.
Why it's Important: Proper vendor data ensures that the raw materials and products you receive have the expected shelf life, leading to better batch and expiration tracking.

4. Inventory Management Data (IM)

Key Fields to Cleanse: Stock Levels: Verify that stock quantities are accurate and up-to-date.
Batch Assignment: Ensure that inventory is correctly linked to the right batch and that batch information (production date, expiry) is accurate.
Why it's Important: Accurate stock and batch-level data are crucial for ensuring that inventory management processes do not allow expired batches to be used or sold.

5. Quality Management (QM) Data

Key Fields to Cleanse: Inspection Lots: Make sure that all inspection lots are correctly assigned to batches and include expiration or shelf life information.
Test Results: Ensure that any quality tests performed on batches reflect true shelf life conditions and are properly recorded.
Defect Codes and Alerts: Cleanse the defect categories to ensure accurate reporting on product quality, particularly when related to expiration issues.
Why it's Important: Ensures the quality of batches is properly tracked and that expired or defective goods are identified before shipment.

6. Production Planning and Control (PP)

Key Fields to Cleanse: Production Order Data: Ensure that production orders contain accurate batch numbers, manufacturing dates, and expected shelf lives.
Routing and Bill of Materials (BOM): Clean and maintain up-to-date data on materials used in batch production, along with their shelf life parameters.
Why it's Important: Production orders must align with batch management and shelf life tracking to ensure product quality and timely usage.

7. Sales and Distribution (SD)

Key Fields to Cleanse: Sales Order Data: Ensure sales orders are correctly linked to the right batches and product shelf life. Shipping Data: Verify that shipping information includes accurate shelf life and batch data to prevent the shipment of expired products.
Why it's Important: Ensures that products are shipped in compliance with their expiration and shelf life constraints.

8. Warehouse Management (WM)

Key Fields to Cleanse: Storage Location Data: Ensure proper categorization of products in different storage locations based on batch and shelf life. Movement Types and Data: Cleanse movement data related to batch transfers and adjustments.
Why it's Important: Warehouse data ensures that products are stored in compliance with their required shelf life conditions, and that batches are rotated correctly.

9. Change Management Data (in SAP GRC)

Key Fields to Cleanse: Change Logs: Ensure accurate tracking of changes made to material and batch master data, particularly when shelf life or batch attributes are adjusted.
Why it's Important: Enables traceability and compliance with audit requirements, especially when shelf life or batch details are modified.

By cleansing these key SAP data objects, you'll ensure accurate tracking of batches, shelf life, and expiration dates, which are critical for improving inventory management, reducing waste, and ensuring compliance.

Data without impact is just noise. How are YOU turning data initiatives into tangible business value? Let's compare notes!

SAP BW/4HANA: The Grey Panther of Data Warehousing

2025-01-27T09:42:37.658000+01:00

SAP BW/4HANA: The Grey Panther of Data Warehousing

“And some things that should not have been forgotten were lost. History became legend. Legend became myth. And for twenty years, the BW passed out of all knowledge.”
(Adapted from Galadriel’s famous words)

Cloud-native data management solutions dominate the presence in many discussions and strategies about data management.

Despite its evolution from BW to BW/4HANA and the significant functional improvements it brought, the solution’s 25-year history— evident in many of its functional corners—makes it difficult for BW/4HANA to be perceived as a modern data warehousing solution.

But there are many concepts in BW that are functionally strong and beneficial, and on which in the last decades many foundations of large and very large BW platforms for many customers of SAP were built.

SAP BW/4HANA addresses challenges that, in the past, may not have been fully recognized as strengths by those who used it, but which are now worth remembering in the discussion about the “modern data stack”.

This article revisits some of these strengths in metadata management, governance, orchestration, and automation—highlighting its utility for (classical) well-structured, large-scale EDWs with a focus on SAP systems as the source of data. This short article concludes by exploring how SAP Datasphere inherits and modernizes some of BW’s proven concepts.

Deep Integration of Metadata Management and Business Semantics

SAP BW/4HANA (within its own functionality) offers unparalleled rich metadata, its management and semantic integration. Its Info Objects always provided rich semantics, time-dependencies, hierarchies, multilingual support, and reusability, hiding a table-oriented view in favor of a more object-oriented perspective. Info Objects embed business semantics directly into data models, eliminating the need for external interventions for that purpose. Features like well-defined amount-to-currency relationships and unit conversions are natively extracted from the source in an automated way, ensuring semantic precision throughout the data chain without having to create additional bespoke logic or external tools to enforce system-wide consistency.

In contrast, many modern cloud platforms often depend on third-party tools for advanced metadata management and lineage, particularly in heterogeneous environments with multiple data warehouse solutions. While these tools can offer benefits in multi-platform setups, they also introduce additional licensing costs and integration complexity. By comparison, BW/4HANA’s integrated approach streamlines unified yet complex data landscapes (of structured data).

Master Data Management

BW/4HANA Info objects of type characteristic have always been an excellent support for master data design and management. Info Objects ensure system-wide uniqueness and consistency, managing time-dependent attributes and hierarchies with minimal developer intervention.

Multilingual support for master data and standard hierarchy containers and operations for even complex, time-dependent hierarchical structures further enhance the utility of Info Objects.

Handling slow-changing dimensions and other master data management tasks are homogeneous across all master data in their application and treatment, reflecting one of BW’s long-standing strengths.

Orchestration and Automation with Process Chains

Process Chains in BW/4HANA automate ETL workflows, ensuring tasks are executed in sequence with built-in error handling, scheduling, and logging. In contrast, cloud platforms often rely on external orchestration tools like Apache Airflow or AWS Step Functions, adding tool complexity to data pipeline design and orchestration. BW/4HANA’s native orchestration has proven to simplify large workflows and to reduce maintenance and monitoring overhead. It also has its effects in the application lifecycle management which is deeply integrated in BW.

Logical Scalability and Dependency Management

BW/4HANA’s previously discussed object-oriented approach enables logical scalability and robust dependency management for complex data landscapes. While cloud platforms like Snowflake focus on elastic scalability, managing interrelated data tables often requires additional tools for cataloguing dependencies. BW/4HANA’s integrated design handles these challenges natively.

While this list highlights key features, it is by no means exhaustive. BW/4HANA offers additional strengths that have made it a robust solution, though they are omitted here for brevity. The point here is that plenty of currently discussed topics and challenges in the use of the “modern data stack”[1] have been resolved many years ago in BW and that for SAP data a solid BW infrastructure still is an asset not to be underestimated in its value when it comes to thoughts about substituting it just for doing something “new”.

SAP Datasphere: Modernizing BW’s Strengths

SAP Datasphere is now showing clear signs of including some of the above-mentioned conceptual strengths of BW/4HANA features. We can find the following concepts in the more recent SAP Datasphere releases:

Multilingual Support: Adapts datasets and reports to user preferences, ensuring global consistency.
Currency Conversion: Replicates BW’s standardized functionality for seamless ERP-DW integration.
Time-Dependent Attributes: Maintains historical master data changes with business semantics like “Valid from” and “Valid to”.
Hierarchies: Incorporates parent-child hierarchies with expanded functionality for modern cloud environments.
Navigation Attributes: Enables logically easy to understand aggregations of fact data by natural “business objects” without duplicating data, ensuring model flexibility and expressiveness.
Non-Cumulative Measures: Supports scenarios like stock levels and balances, leveraging delta records for precision.
BW-Style Measure Types: Includes calculated, restricted, and currency conversion measures, maintaining analytic rigor.
Variables: Enhances interactivity and flexibility through dynamic parameter settings with a standardized behavior.

Conclusion

SAP BW/4HANA’s integrated approach included solutions for many challenges in metadata management, governance, and scalability, reducing complexity and homogeneous solution patterns in large-scale deployments. While cloud platforms necessitate often third-party tools for similar capabilities, BW/4HANA offers a cohesive solution which his not easily found anywhere else. Despite being labelled as a ‘legacy’ system, BW/4HANA remains and will remain relevant for many organizations for years. Transitioning its proven solutions to a “modern data stack” would require significant effort, discipline, re-skilling and governance, making BW/4HANA a practical choice for the foreseeable future.

SAP Datasphere represents a powerful evolution, blending modern cloud-native architecture with some of the proven concepts of BW/4HANA. It not only supports seamless SAP data integration but also accommodates diverse data sources, offering enterprises a balance of innovation and reliability.

[1] A name which implicitly includes the multi-tool nature of it

Details of SAP Data and Analytics Advisory Methodology Phase IV (Data Governance & Roadmaps)

2025-01-29T14:32:48.145000+01:00

In the previous blogs, I have provided details of the first three phases of the methodology for developing a target architecture for data-driven business outcomes:

Phase I: Scoping & Baseline Analysis
Phase II: Business Outcomes & Solution Requirements
Phase III: Capability Map & Solution Architecture

This blog is about the final phase, with focus on data governance and roadmap planning.

Overview of phase IV

Phase IV concludes your exploration of a new target architecture by assessing your data governance and organizational aspects and creating a roadmap or project plan for implementing the new architecture.

In the first two steps of this phase, you investigate relevant data governance and organizational aspects by applying the “Data and Analytics Maturity Assessment”. This topic is important as it is a key enabler to successfully establish the chosen architecture und realize the business outcomes defined in phase II.

Step 3 of this phase describes how to realize the data & analytics solution architecture by consolidating and planning all required activities and creating a high-level roadmap or project plan.

Steps IV.1 & 2: Data Governance / Roles & Organization

I have already discussed the details of the “Data and Analytics Maturity Assessment” in the previous blog. It is used to assess the maturity of data governance (step IV.1) and organizational aspects (step IV.2) and to derive the necessary improvement actions from the gaps between the current and desired maturity levels.

The activities identified during the assessment are consolidated and made available for the final step, roadmap planning.

Here is an example:

Step IV.3: Roadmaps/Project Plan

A roadmap is a strategic planning tool designed to align short-term and long-term goals with specific IT solutions. It presents these plans in formats, such as a sunray diagram or a planning chart.

An architecture roadmap organizes activities based on business needs and priorities to achieve the target architecture within a defined time frame. These activities are grouped into functional clusters to streamline planning and execution.

You start by collecting the activities to realize the defined solution architecture in an activity catalog. Each activity is defined with a name, description, and owner. Next, you review the recommended actions from the Data & Analytics Maturity Assessment, break them down into more detailed activities if necessary and add them into the activity catalog.

Then, you organize the activities according to logical clusters. You can define clusters by grouping activities that belong to the same context.

Examples of such clusters include:

Architecture Validation (PoC)
Implementation
Operations
Security
Know-How Transfer
Organization and Governance

Next, you order the activities and identify dependencies to other activities. Finally, you estimate a high-level timeline.

Here is an example of an activity catalogue:

The roadmap is then created based on the activity catalog. The following example shows the roadmap in a sunray diagram.

This completes the Data & Analytics Advisory Methodology. The next step could be the preparation of an implementation project / program where the roadmap is thoroughly planned, resources are assigned, and the architecture design is detailed out.

This article concludes the blog series that introduced the methodology and detailed the phases and steps. But this is not the end,…

…as the methodology is now available in the SAP Help Portal. It provides a comprehensive guidance on how to apply the methodology and includes the content and templates that are provided in the SAP Workzone.

We appreciate any feedback on the methodology and its documentation. We are especially interested in hearing about your experiences using the methodology. Please contact us by e-mail at sap-data-analytics-methodology@sap.com.

Recreating Source system in RSA1 after BW Refresh

2025-02-11T15:07:35.406000+01:00

Hello Everyone,

Welcome to my blog.

During a recent refresh of the BW system, I encountered an issue during the BDLS step where BDLS failed. After analyzing the logs, I discovered that some tables have duplicate entries.

We also faced issues with the RSA1 configuration. We deleted the old source system and created a new one.

Entries of the source system can be seen in TABLE - RSBASIDOC.

Please remember that SAP does not recommend manual changes or modifications to any SAP standard table.

There are the below steps given below to recreate Source System.

Make sure that logical name should be created or converted after refresh.
All relevant RFC are working fine in BW system and Source system, if not than fixed it first like in this case, we are creating 2 new source system (ERD and SRD) so RFC will be (source system, source system_dialog). Like below

4. After that, we have to create new source system in rsa1.
Click on create in BW.
Existing source system RFC
Give ALEREMOTE and BWREMOTE password.

Click on no

click on continue

Click on use

After that System will be created.

5. Post creation we will restore

Give password of both system

click on continue

now click on use

click on activate

Click on yes

6. Now we’ll check

Reference notes

2898275 - https://me.sap.com/notes/0002898275

2111337 - https://me.sap.com/notes/2111337/E

Things that keep in mind.

It would be better that we’ll make TOC of these tables RSBASIDOC, RSLOGSYSDEST, RSLOGSYSMAP
Screenshot of RSA1 will be taken.

Exploring the Potential of SAP Business Data Cloud

2025-02-13T20:52:22.180000+01:00

Why SAP Business Data Cloud Matters

Data is the backbone of modern businesses. Companies that effectively analyze and leverage their data unlock valuable insights, much like striking gold—the deeper the analysis, the greater the rewards. SAP Business Data Cloud aims to break down data silos, allowing businesses to seamlessly connect data across SAP applications, third-party sources, and multiple business domains.

One of the most significant capabilities of SAP Business Data Cloud is its integration with Databricks, a market leader in data engineering and AI-powered analytics. This collaboration enables businesses to generate domain-specific insights by combining structured and unstructured data while reducing redundancy. Unlike traditional data solutions that require multiple copies of the same dataset, SAP Business Data Cloud enables zero-copy data access, making it particularly valuable for enterprises dealing with petabytes of data.

How Joule Enhances SAP Business Data Cloud

One of the standout features of SAP Business Data Cloud is its integration with Joule, SAP’s business AI assistant. What sets Joule apart from traditional Large Language Models (LLMs) is its deep business context and enterprise-grade data intelligence.

Joule’s architecture operates through the following key components:

User Request Processing

When a user submits a request, Joule understands the prompt, analyzes relevant business scenarios, and applies data filtering via the Knowledge Catalog.

Knowledge Catalog

This repository contains both SAP data and customer-specific data, ensuring that responses are tailored to business needs.

Retrieval Augmented Generation for Enterprises (RAGe)

The output process leverages RAGe, an advanced AI methodology, ensuring responses are generated with reliable, context-aware business insights.

AI Hub and Vector Engine

The AI Hub integrates business context with SAP data, enhancing Joule’s ability to deliver accurate, business-centric queries from LLMs.

Driving Business Success with Unified Data

With SAP Business Data Cloud, Joule, and Databricks, organizations can unify enterprise data sources and unlock deeper insights tailored to various Lines of Business (LOBs). This intelligent data unification will empower companies to:

Eliminate redundant data storage
Generate multi-dimensional insights
Enhance AI-driven decision-making
Unlock revenue potential through data-driven strategies

By leveraging AI-powered analytics and zero-copy data integration, SAP is setting the stage for the next era of business intelligence, enabling companies to maximize data value while maintaining efficiency and security.

Here is the link SAP Business Data Cloud preview

Dynamic Derivation of System ID from Application Server Directory (AL11)

2025-02-25T19:03:00.224000+01:00

Background

There are certain cases where other non-SAP applications will upload the file as Inbound in Application Server which needs to be loaded into BW. We have dedicated path created in AL11 where these Inbound files are placed and to be more precise, we define the directory path name based on System Landscape (DEV, QAT, PRD). This is done for the purpose to know exactly in which system either Inbound files are placed, or Outbound files are generated via OHD or any Custom Abap Programs.

For Example, the AL11 path name in DEV Environment is /usr/sap/actual/temp/ABD/test.txt

The AL11 path name in Quality Environment is /usr/sap/actual/temp/ABQ/test.txt

The AL11 path name in Production Environment is /usr/sap/actual/temp/ABP/test.txt

Here the System ID for DEV Environment is ABD

Here the System ID for Quality Environment is ABQ

Here the System ID for Production Environment is ABP

Scenario -1 (Info-package Routines for loading Inbound Flat Files)

Loading Data into BW via Dynamic Path in Info-package level where System ID is derived dynamically based on the System Landscape.

Below is the Syntax to dynamically derive the System ID.

CONCATENATE '/usr/sap/actual/temp/' sy-sysid '/test.txt' INTO p_filename.

Scenario -2 (Open Hub Destinations)

Generating the Outbound files via Open Hub Destination in Application Server path. Below is the Syntax of the Physical path under the Logical File Path Definition to dynamically derive the System ID. T. code is FILE.

/usr/sap/actual/temp/<SYSID>/<FILENAME>

Scenario -3 (Operating System Command)

Sometimes we need to execute a Script which is placed in the Application Server via System Command. This System Command is created in T. Code – SM69. It's being used to create/maintain external operating system commands.

Below is the Syntax to dynamically derive the System ID in Operating System Command definition to locate the exact path.

/usr/sap/actual/temp/$/SAPSYSTEMNAME//scripts/test.sh

Creating OData Service with SAP BW Query for C4C Integration via Cloud Platform Integration (CPI)

2025-03-20T18:32:15.806000+01:00

Introduction:

For the requirements such as sending the BW Data (Query) / OData to any SAP cloud systems or third party systems using the Integration Platform process, I created OData Service using SAP BW Query outbound of BW system and send the data to SAP C4C system using SAP CPI (Cloud Platform Integration) by following steps.

Step 1 Create a BW Report:

Go to the required BW Query in BW Modeling Tools and enable the option By OData from BW Query Settings as shown below:

Refer : Steps to Create an ODATA service for a BW Query | SAP Help Portal

Tip: Sometimes, variables requiring mandatory user input may prevent the OData Service from enabling. Ideally, use optional or no variables and avoid authorization-relevant ones. However, this issue may not be relevant in the latest SP levels.

Step 2 Enable OData Service:

2.1 Go to T-Code in BW system: /n/IWFND/MAINT_SERVICE → Select “Add Service”

Select the Service with Query Name
For Ex: - Report Name: ZSALES_ODATA_BW_QUERY
Service generated: ZSALES_ODATA_BW_QUERY_SRV

Service is automatically created & Make sure to Enter your package to collect in a transport request.

2.2 Go back one step to the Service Catalog (Activate and Maintenance) screen:
Select the Service ZSALES_ODATA_BW_QUERY_SRV -> Call Browser to check the data.

2.3 Go to SICF t-code to activate the service

2.4 Check the URL and make sure the Query runs fine without any issues:

By default, the service is called with parameter '?$format=xml', the full URI is: /sap/opu/odata/sap/<query name>_SRV/?$format=xml

Here we can see, with parameter 'ZSALES_ODATA_BW_QUERYResults' we can access the data in the query result.

2.5 Below OData Service links can be useful to display Query Information:

Display Metadata:

https://<BI Portal URL>/sap/opu/odata/sap/ZSALES_ODATA_BW_QUERY_SRV/$metadata

Display Format:
https://<BI Portal URL>/sap/opu/odata/sap/ZSALES_ODATA_BW_QUERY_SRV/?$format=xml

Display Query Result:

https://<BI Portal URL>/sap/opu/odata/sap/ZSALES_ODATA_BW_QUERY_SRV/ZSALES_ODATA_BW_QUERYResults

Display Query Result with Filters:
https://<BI Portal URL>/sap/opu/odata/sap/ZSALES_ODATA_BW_QUERY_SRV/ZSALES_ODATA_BW_QUERYResults?$filter= 0CALMONTH eq '04.2025'

Step3 Login to the SAP CPI (Cloud Platform Integration) system:

3.1 Field mapping between the BW Query and C4C in CPI system.

Map the BW Query Result (Fields from left side of below mapping) with the C4C OData API (Cloud Data Source) from C4C system as shown below:

Note: C4C OData API (Cloud Data Source) is created in C4C system and the BW Query fields are mapped in CPI system.

3.2 A daily job can be scheduled to load the data from SAP BW Query OData service to SAP C4C Cloud data source using CPI system. A defined period and time can also be set similar to process chains and update the data regularly.

Please Refer: SAP Cloud for Customer - Integration to Central An... - SAP Community

Also Please refer to below Links for more SAP insights on OData Service Enabling:

Steps to Create an ODATA service for a BW Query - SAP NetWeaver Business Warehouse - Community Wiki

How to do Odata Services from BEx Query | SAP Blogs

Transform SAP BW Queries into OData Service: Part 1 – Generation and Activation | SAP Blogs

ODP-Based Data Extraction via OData - SAP Help Portal

Flip Virtual Tables in Calculation Views from ECC to S/4HANA

2025-04-10T16:54:07.764000+02:00

As SAP prepares to end support for ECC by 2027, many companies are making the crucial transition to S/4HANA. This shift promises enhanced capabilities and performance, but it also brings its own set of challenges. One of the most significant hurdles involves the calculation views that rely on virtual tables from the ECC system. After migration, these views need to be redirected to the new S/4 system.

For many organizations, BW hybrid modeling is a common practice. This approach uses virtual tables from the ECC source system within their calculation views. With numerous calculation views depending on these ECC virtual tables, the need to point them to the new S/4 system without disrupting functionality becomes paramount.

SAP Note 3103359 clearly states that there is no straightforward way to change the adapter and remote source database without dropping and recreating the remote source. This raises a critical question: **Do we need to adjust all the calculation views?**

Adjusting all calculation views is a daunting task. It requires extensive manual effort and rigorous testing, which inevitably adds to the project's cost. The prospect of manually updating each view can be overwhelming for many organizations.

However, there is a workaround that can simplify this process. By deleting the virtual table and recreating it within the same schema, organizations can effectively redirect their calculation views to the new S/4 system.

Example: Redirecting Virtual Tables in HANA System

Let's take a closer look at a practical example. Below, we see the HANA system with the "ECC" schema. There are two virtual tables, "V_EKKN" and "V_EKKO" which refer to the ECC tables "EKKN" and "EKKO".

Now, we want to point these tables to the new S/4 system. We can achieve this by following these steps:

Delete the virtual tables.
Recreate the virtual tables.

These steps can be performed by running the following script:

DROP TABLE <schema_name>."<virtual_table_name>" CASCADE;
CREATE VIRTUAL TABLE "<schema_name>"."<virtual_table_name>" at "<remote_source>"."<database_name>"."<remote_schema_name>"."<remote_table_name>";

DROP TABLE ECC."V_EKKN" CASCADE;
CREATE VIRTUAL TABLE "ECC"."V_EKKN" at "S4X"."S4DB"."SAPPROD"."EKKO";

DROP TABLE ECC."V_EKKO" CASCADE;
CREATE VIRTUAL TABLE "ECC"."V_EKKO" at "S4X"."S4DB"."SAPPROD"."EKKO";

By executing this script, we can see that the virtual tables are now pointing to the new S/4 system "S4X."

By deleting the virtual table and recreating it within the same schema, organizations can seamlessly redirect their calculation views to the new S/4 system without needing to adjust the calculation view itself. This method, while still requiring careful execution, can significantly reduce the complexity and effort involved.

Reference links:
SAP Help portal for Managing Virtual Tables
SAP Note 2542963 - Is it possible to rename a Remote Source in SAP HANA?

Easy to get in SAP

2025-09-22T22:39:15.645000+02:00

🚀 3 Simple Steps to Break Into the SAP World (No Overwhelm!)
Want to join the $300B+ SAP ecosystem but don’t know where to start? Here’s how to dive in without complexity:

1️⃣ Start with FREE Learning
- Explore SAP Learning Hub (free tier): Bite-sized courses on ERP, S/4HANA, and cloud solutions.
- Try SAP Community: Tutorials, forums, and real-world use cases. No cost, just curiosity!

2️⃣ Pick Your Path
- For Tech Folks: Learn ABAP (coding) or SAP BTP (low-code/no-code tools).
- For Business Roles: Focus on Finance (FI), Supply Chain (MM), or HR (HCM) modules.
- Tip: Start with one module (e.g., SAP S/4HANA Cloud) instead of boiling the ocean.*

3️⃣ Get Hands-On FAST
- Use SAP Free Trials: Test real systems via GROW with SAP (for new users) or RISE with SAP sandbox.
- Join SAP’s Partner Ecosystem: Many offer free workshops and certifications (e.g., SAP Learning Journeys).

💡 Key Mindset Shift
"SAP isn’t just for IT pros—it’s for *problem-solvers. If you understand business processes (finance, logistics, sales), you’re already halfway there."

✅ Your First Move Today:
👉 Bookmark: [SAP Learning](https://learning.sap.com)
👉 Join: [SAP Community](https://community.sap.com)
👉 Try: Free SAP S/4HANA Cloud trial (search "SAP Free Trials").
and don't forget to look at Youtube 😃

You don’t need a Ph.D. in tech—just a structured start. What’s your SAP goal? Let’s chat below! 👇

Warm Wishes 💖

SAP BTP: Data Fabric

2025-12-12T12:50:49.920000+01:00

Irrespective of where your source system resides, SAP provides the capabilities to ingest, harmonize and unlock real-time and predictive insights on your target system with confidence. This is supported by resilient, enterprise-grade frameworks, tools, and methodologies that safeguard data quality, integrity, and consistency throughout the entire data lifecycle.

SAP. (2023). Figure:1

Source Systems for Data Provisioning in SAP

URL: https://learning.sap.com/courses/implementing-data-provisioning-in-sap-bw-4hana/identifying-sap-bw-4hana-source-systems-and-their-use-cases

The endless possibilities of raw, intrinsic data ingestion from e.g. Enterprise warehouses, data lakes, rational & in-memory databases, SaaS applications even social media into SAP HANA (On-premises and/ or Cloud) opens a world of opportunity for data engineers to acquire rich, accurate, and complete master and transactional data from all possible sources imaginable. Responsibility for managing the data acquisition processes depends on the provisioning type being used. Common approaches include:

Provisioning Type	Data Flow Responsibility	Metadata Management	Typical Use Cases
Application-Controlled	Source application pushes data to target using APIs or connectors.	Managed in application layer (schema, mappings, transformations).	Custom logic, application-driven workflows, SaaS integrations.
Change Data Capture (CDC)	CDC engine streams changes (insert/update/delete) from source to target in near real-time.	Managed by CDC tool (change logs, timestamps, transaction markers).	Real-time replication, incremental updates, event-driven architectures.
Database-Controlled	Target database pulls data from source using adapters or SDI.	Centralized in database layer (schemas, lineage, governance).	Governance-heavy environments, centralized data lakes, enterprise ETL.

Figure:2

Provisioning Types: Data Flow & Metadata Management

The utilization of high-performance ETL/ELT tools ensure the consistence, quality and reliability of the data is upheld and thus often used to unify the data to best meet the objectives of the stakeholder’s request either being executives, decision makers, marketing or product. A well-thought-out data provisioning strategy should always take precedence as this ensure a smooth flowing data process. Depending on the usage of the data, a data engineer may explorer the below different data provisioning approaches:

Data federation – Data is not loaded nor persisted into the target system (SAP HANA) but rather read directly from the source system only when the data is needed for use.
Data replication – Data from the source system is loaded, persisted into the target system and kept in sync using various mechanisms
- Real-time (Synchronous) replication
- Delta load (asynchronous)replication
Data transformation – The raw data is enhanced with additional features e.g. calculation columns, renamed fields or new fields derived from other exiting fields

SAP provides but not limited to the below high-performance native data integration technologies:

Tool	Primary Function	Position in Landscape Architecture
SLT (SAP Landscape Transformation Replication)	Real-time replication (CDC)	Integration layer between SAP sources and HANA DB
SDA (Smart Data Access)	Virtual access to remote sources (federation)	Integration layer enabling query pushdown to the HANA DB
SDI (Smart Data Integration)	ETL/ELT pipelines, adapters, dataflows	Integration layer moving and transforming data before loading into the HANA DB

Figure 3:

SAP Native ETL/ELT tools

Furthermore, for Data quality, cleansing and enrichment before persisting into the HANA DB, SAP provides the below:

Smart Data quality (SDQ) for SAP HANA on-premises
Data Quality Management Microservices (DQMm) as a subscription-based microservice for SAP HANA Cloud

SAP Hana provides a variation of consolidated tools both on premise and in the cloud, all providing for various functionality for different use cases such as:

Supporting data provisioning
Developing Database artefacts
Monitoring the performance of the develop artefacts
Monitoring the Database and its system performance
Administering and managing the Database

One of the key strengths of the SAP ecosystem is the ability to seamlessly combine cloud functionalities together with on-premises and still import specific/ needed applications as micro services into common IDE’s such as Microsoft Visual Studio code is commendable because it:

Provides business with a comprehensive 360-degree view of
- Database health overview.
- Data life cycle from end to end.
- Application life cycle from end to end.

Empowers the end users, data engineers, developers, data scientists, data analyst and support engineers with greater power and flexibility over the data being used.

Below are native tools used by SAP for various purposes:

SAP Business Application studio – Cloud development tool.
Web IDE – On-premises development tool
SAP Hana cockpit - a web-based tool for monitoring, administering, and managing SAP HANA databases, offering a centralized, browser-accessible interface with tile-based dashboards for performance, resource usage (CPU, memory, disk), alerts, and security. It allows tasks like starting/stopping services, managing users, viewing alerts, analyzing performance, and running SQL queries through its integrated Database Explorer.

SAP SaaS applications are imported into common IDE, s such as Microsoft Visual Studio code via extensions, libraries etc. for a specific purpose e.g.:

Supportability tools for SAP HANA - For both online and offline performance analysis and troubleshooting. With a focus on the needs of support engineers, the performance analysis tools provide enhanced analysis options for trace files and runtime dump files, as well as allowing in-depth analysis on extensive datasets.
The SQL analyzer tool for SAP HANA - A performance analysis tool that helps developers visualize and understand SQL execution plans. Designed for query tuning and optimization, it offers detailed insights into how queries run within the SAP HANA database.
SAP HANA Database Explorer - Provides functionality for accessing SAP HANA databases, browsing the database catalog, and executing SQL from a SQL console.

Figure 4:

SAP Development and Administration Tools Overview

The consolidated toolset further enhances productivity and convenience for end users, data engineers, developers, data scientists, data analyst and support engineers by enabling them to access all essential capabilities from a single unified platform. The choice and flexibility to work with the desired programming languages ranging from but not limited to: SQL, SQL Script, Python, Node.js, JavaScript and react native etc. Enable a cross-functional team with diverse skill set to collaborate effectively while striving towards the same common goal.

Figure 5:

Core Programming Languages and Frameworks for SAP HANA Cloud and On-Premises

After successfully completing the Explore phase, with a solid fit -to-standard analysis of proposed use cases, Extrinsic data pipelines are then implemented for reliable and consistent data flow. These pipelines support a wide range of business needs such as decision making, go-to-market activities or ML/AI use cases requested by various stakeholders. The process is typically executed and maintained by data engineers, developers, data scientists, data analyst and/or support engineers.

The harmonized data is then governed, analyzed, and visualized through multiple front-end applications. These applications are powered by various integration frameworks e.g. REST APIs, OData v4, JDBC/ODBC, each serving different user needs and functional objectives:

Figure: 6

SAP-Compatible Applications and Their Integration Frameworks

Mutatis mutandis, Data is the inception of all there is, as it has been in the beginning, is now and ever shall be. Thank you all for taking a glimpse into the above Content. Please don’t for get to like, comment and share.

Abbreviations:

API: Application Programming Interface

BTP: Business Technology Platform

CDC: Change Data Capture

DB: Database

DQMm: Data Quality Management Microservices

ELT: Extract, Load, Transform

ETL: Extract, Transform, Load

IDE: Integrated Development Environment

ML/AI: Machine Learning/Artificial Intelligence

ODBC: Open Database Connectivity

OData: Open Data Protocol

REST: Representational State Transfer

SaaS: Software as a Service

SDI: Smart Data Integration

SDA: Smart Data Access

SDQ: Smart Data Quality

SLT: SAP Landscape Transformation Replication

SQL: Structured Query Language

Reference:

SAP. (2023). Figure:1 Source Systems for Data Provisioning in SAP, URL: https://learning.sap.com/courses/implementing-data-provisioning-in-sap-bw-4hana/identifying-sap-bw-4hana-source-systems-and-their-use-cases

How to Learn SAP BW/4HANA and Get Certified

2026-01-14T15:48:54.550000+01:00

How to Learn SAP BW/4HANA and Get Certified

SAP BW∕4HANA offers a managed approach to data warehousing. This means that prefabricated templates (building blocks) are offered for building a data warehouse in a standardized way.

So how can you become proficient in SAP BW/4HANA?

The SAP Certified Associate - Data Engineer - SAP BW/4HANA certification verifies that you possess the core skills required in the role of a Data Engineer in the SAP BW/4HANA area. It proves that you have an overall understanding and in-depth technical skills to participate as a member of a project team in a mentored role. The certification is recommended as an entry-level qualification.

The following diagram shows the Data Engineer role responsibilities compared with the Data Analyst/Data Scientist role.

Online Self Study

For online self study, you can use the learning journey Exploring SAP BW/4HANA to prepare for the certification.

In this learning journey, you build your knowledge step-by-step with online courses, containing explanations using texts, videos and exercise simulations.

The following online courses are needed to prepare for the certification:

Getting Started Building an On-Premise Data Warehouse using SAP BW/4HANA
Creating Queries in SAP BW/4HANA
Implementing a Data Warehouse Using SAP BW/4HANA
Implementing Modeling Scenarios in SAP BW/4HANA
Implementing Data Provisioning in SAP BW/4HANA
Implementing Authorizations in SAP BW/4HANA

Instructor-led Classroom

As an alternative, you can attend instructor-led classroom trainings (on-site or virtual), where you can also work through lots of exercises on a provided SAP system.

The following instructor-led trainings are needed to prepare for the certification:

BW405: SAP BW/4HANA Query Design and Analysis
BW410: SAP BW/4HANA Data Warehousing
BW430: SAP BW/4HANA Data Modeling
BW450: SAP BW/4HANA Data Acquisition
BW465: SAP BW/4HANA Authorizations

Hands-on Practice

If you want to practice outside the setting of a classroom training, SAP Practice Systems offers to practice using the similar exercises that are used in the instructor-led class.

Just click below to explore practice systems:

https://learning.sap.com/practice-systems?page=1

Live Sessions

In addition to courses and practice systems, the SAP Learning portal offers live sessions that are useful for learning guidelines, ask questions, and deepen your knowledge on specific topics. These sessions are delivered by subject-matter-experts (SMEs), either from SAP Training, SAP Partners or other experts at SAP such as Product Managers.

The sessions come under five categories:

Get Started
Expert Deep Dive
What's New
Get Certified
Stay Certified

Just click below to explore the live sessions:

https://learning.sap.com/live-sessions?page=1

Certification Exam

When you feel prepared enough, it's time to take the certification exam.

On this topic, there is some news. SAP is moving its certification model towards practice-based exams which will replace traditional theoretical/knowledge-based exams (multi-choice questions), starting January 1st 2026. Some pilot exams are already available since the beginning of November 2025.

Community

We are looking forward to getting your feedback on these resources aimed at getting your level higher in the area of SAP BW/4HANA. Please feel free to use this new community and connect with us and other learners. Also let us know about topics you would like to have covered in future live sessions, so that we can make our content even more suited to your needs.

Happy learning with SAP!

Eric Kuijvenhoven

SAP Practice and Certification Journey

2026-01-27T11:44:54.201000+01:00

In today’s fast-evolving logistics industry, digital transformation and integrated systems play a critical role in ensuring efficiency, transparency, and sustainability. As someone who has received formal education in logistics and is actively continuing my professional development in this field, I strongly believe that learning SAP S/4HANA Transportation Management (TM) is a natural and strategic step in my career journey.

My motivation to learn SAP stems from my desire to deepen my understanding of logistics beyond operational processes and into system-driven decision making. Transportation Management is one of the core pillars of supply chain operations, and SAP S/4HANA TM provides an end-to-end perspective—from planning and execution to cost settlement and performance analysis. Gaining hands-on practice with this system will allow me to better understand how logistics processes are designed, optimized, and managed within global organizations.

Additionally, learning SAP S/4HANA TM will help me develop a more analytical and structured mindset. Exposure to different ERP systems not only enhances technical competence but also enables logistics professionals to think holistically, connecting operations, finance, and strategy. I believe this knowledge will significantly contribute to my ability to analyze complex logistics scenarios and propose effective, data-driven solutions.

Through SAP Practice and Certification, my goal is to strengthen my professional skill set, bridge the gap between theory and real-world applications, and position myself as a more capable and future-ready logistics profession

BI Remediation Checks during ECC to S/4HANA Migration

2026-03-02T07:22:03.809000+01:00

As SAP prepares to end support for ECC by 2027, many organizations are making the crucial move to SAP S/4HANA. The shift can unlock improved capabilities and performance, but it can also introduce changes that ripple through the BI landscape if we don’t plan for them early.

From a BI perspective, there are multiple aspects to consider during the transition. This blog focuses on the BI remediation checks that help protect data trust, reporting, and decision-making continuity during and after the migration.

Why do we need BI remediation?
The migration from SAP ECC to SAP S/4HANA introduces significant changes to the underlying data structures. These changes impact how data is extracted, modelled, and consumed in the BI system. Because of that, reporting remediation becomes critical to ensure data consistency, accuracy, and continuity in analytics after the migration.

With remediation, we ensure:

BI models and reports remain functional and aligned with the new S/4HANA structures
Disruption is minimized for business operations and reporting processes

Data model simplifications in S/4HANA (what changes underneath)
When SAP S/4HANA was first introduced, the SAP ERP 6.0 data model underwent major restructuring. Redundancies were removed, and many smaller tables were consolidated into fewer, larger ones.
Figure 1 can help visualize the concept and why “what worked in ECC” may not behave the same way in S/4HANA.

FIGURE 1

Impact of S/4HANA migration on the BI system (what to check)

1) Impact on BW Datasources / Extractors
If you run SAP BW, one of the first questions is:
“After ECC becomes S/4HANA, will my extractors and DataSources still be supported and behave the same way?”
SAP note 2500202 provides an Excel sheet produced by SAP that details the impact of S/4HANA on BW Datasources. This sheet is periodically updated. The same note also contains a PPT that guides us on how to interpret and use the Excel list.

A proven approach is:

Inventory your active extractors (e.g., via ROOSOURCE).
Identify which are truly used (process chains / BI statistics where available)
Cross-check them against the SAP note 2500202 list and categorize outcomes (no change / restricted / obsolete / alternative required)

2) Tables with Replacement Objects (don’t rely on “classic tables” blindly)
If, as part of your BI remediation strategy, you plan to continue using certain classic ECC tables, for example, relying on MARD instead of adopting the newer MATDOC structure, you must depend on the corresponding Replacement Object rather than accessing the table directly.
With the data model simplification in SAP S/4HANA, many traditional ECC tables are no longer the authoritative “single source of truth.” In many cases, data is aggregated or derived via CDS views that join and enrich information from multiple sources. Because of this shift, it becomes essential to transition from reading classic tables (such as MARC, MARD, and others) to using their designated Replacement Objects, ensuring that analytics and integrations receive accurate, up‑to‑date data.

In Native HANA, it is advisable to verify whether a table has a replacement object or whether it can still be used directly.
In SAP BW, the datasource typically absorbs most of the underlying data model changes. However, in hybrid scenarios, particularly when calculation views are built on ECC base tables or when custom datasources built on ECC base tables, it becomes essential to thoroughly assess the impact on the affected tables.

Figure 2 shows an example for table MARD. NSDM_E_MARD serves as the replacement view for the classic MARD table. Although data continues to reside in MARD, those entries generally represent older documents migrated to S/4HANA or new S/4HANA records that are stored without measure fields. To obtain the full set of information (both characteristics and measures) for transactions created in S/4HANA, you must use the replacement object, in this case NSDM_E_MARD, rather than relying on MARD directly.

FIGURE 2

3) Replacement of obsolete tables
Some ECC tables are replaced or made obsolete, and direct reporting on such tables is no longer recommended or supported. Examples tables include:

KONV, VBUP, VBUK, EIKP, EIPO

Obsolete tables may still exist, but they can behave as compatibility views. For accuracy and performance, they should be switched to transparent tables where applicable.

SAP Note 3250204: describes about the replacement table PRDC_ELEMENTS for KNOV table.
SAP Note 2198647: talks about tables VBUP, VBUK and SD data model changes.
SAP Note 3482527: covers about new S/4HANA foreign trade.

4) Key fields changed (primary key shifts can break pipelines)
In S/4HANA, the primary keys of some tables have changed compared to ECC. Some examples are:

VBFA
FAAV_ANLC

These changes directly affect how data is stored, identified, and retrieved. That can break:

Joins in HANA models
Deduplication logic
Delta logic or key-based transformations in BW

Action

Review where these tables are used in extraction/transformation layers
Re-check join conditions, uniqueness assumptions, and surrogate key logic
Re-test deltas and aggregations end-to-end

5) Field length extensions (small change, big downstream effect)
Some fields were extended in S/4HANA to support broader functional requirements, enhanced integration needs, and future extensibility.
One such example is:

VBAP-VBTYP (Char1) Document Category replaced by VBAP-VBTYPL (Char4)

SAP Note 2495386 details the BW datasource that uses this object.

Another major example:

Material Number (SAP Note 2215424) changed from char(18) to char(40)

A key design decision regarding material numbers is whether your organization will:

Adopt a true 40‑character material number, introducing a new business meaning, or
Retain the existing 18‑character logic, while simply expanding the technical field length to 40 characters.

The chosen approach directly influences how material numbers flow through your BI data models and determines the adjustments required in downstream reporting and integrations.

6) Centralized Master Data via Business Partner (Customer/Vendor integration)
SAP fundamentally changed how master data is managed by introducing a unified, centralized model for customers, vendors, and business partners. This replaces the traditional ECC siloed structures with a harmonized object: the Business Partner (BP).
It’s not a “technical-only” change, it’s a semantic change in how master data is governed
Please check the SAP Note 2265093 note for more information.

7) Redirecting ECC Virtual Tables in HANA Calculation Views
When your BI models use calculation views that reference ECC base tables, these views must be updated after the migration to ensure they point to the corresponding objects in the S/4HANA system. Redirecting them is essential to prevent calculation view failures and to avoid inconsistent or incorrect analytical results after the migration. Please refer the blog for mitigation of this impact.

Final thoughts: ECC to S/4HANA migration is not only an ERP transformation, but also a BI continuity challenge. The safest approach is to treat BI remediation as a structured workstream, validating extractors, replacing obsolete dependencies, and proactively addressing table/key/field changes before they surface as production reporting issues.

If this post helps you structure your BI remediation checks, please consider sharing your own experiences or additions in the comments.